The solvent uptakes and stabilities to the guest evacuation were studied and compared for 1D and 2D coordination networks. The de-solvated forms revealed a significant increase of emission in comparison with the as-synthesized crystals.Thin-film n-n nanoheterostructures of SnO2/TiO2, highly sensitive to NO2, were obtained in a two-step process (i) magnetron sputtering, MS followed by (ii) Langmuir-Blodgett, L-B, technique. Thick (200 nm) SnO2 base layers were deposited by MS and subsequently overcoated with a thin and discontinuous TiO2 film by means of L-B. Rutile nanopowder spread over the ethanol/chloroform/water formed a suspension, which was used as a source in L-B method. The morphology, crystallographic and electronic properties of the prepared sensors were studied by scanning electron microscopy, SEM, X-ray diffraction, XRD in glancing incidence geometry, GID, X-ray photoemission spectroscopy, XPS, and uv-vis-nir spectrophotometry, respectively. It was found that amorphous SnO2 films responded to relatively low concentrations of NO2 of about 200 ppb. A change of more than two orders of magnitude in the electrical resistivity upon exposure to NO2 was further enhanced in SnO2/TiO2 n-n nanoheterostructures. The best sensor responses RNO2/R0 were obtained at the lowest operating temperatures of about 120 °C, which is typical for nanomaterials. Response (recovery) times to 400 ppb NO2 were determined as a function of the operating temperature and indicated a significant decrease from 62 (42) s at 123 °C to 12 (19) s at 385 °C A much smaller sensitivity to H2 was observed, which might be advantageous for selective detection of nitrogen oxides. The influence of humidity on the NO2 response was demonstrated to be significantly below 150 °C and systematically decreased upon increase in the operating temperature up to 400 °C.Bacterial leaf streak (BLS) caused by Xanthomonas oryzae pv. oryzicola (Xoc) is one of the most devastating diseases in rice production areas, especially in humid tropical and subtropical zones throughout Asia and worldwide. A genome-wide association study (GWAS) analysis conducted on a collection of 236 diverse rice accessions, mainly indica varieties, identified 12 quantitative trait loci (QTLs) on chromosomes 1, 2, 3, 4, 5, 8, 9 and 11, conferring resistance to five representative isolates of Thai Xoc. Of these, five QTLs conferred resistance to more than one Xoc isolates. Two QTLs, qBLS5.1 and qBLS2.3, were considered promising QTLs for broad-spectrum resistance to BLS. The xa5 gene was proposed as a potential candidate gene for qBLS5.1 and three genes, encoding pectinesterase inhibitor (OsPEI), eukaryotic zinc-binding protein (OsRAR1), and NDP epimerase function, were proposed as candidate genes for qBLS2.3. Results from this study provide an insight into the potential QTLs and candidate genes for BLS resistance in rice. The recessive xa5 gene is suggested as a potential candidate for strong influence on broad-spectrum resistance and as a focal target in rice breeding programs for BLS resistance.Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49-0.50 for PM10 and 0.46-0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.Resilience is defined as the capacity to cope successfully with change or adversity. The aims of our study were to investigate levels of resilience in Italian healthcare professionals (HCPs) during the Coronavirus disease 2019 (COVID-19) pandemic and to identify potential predictors of resilience.
We performed a web-based survey of HCPs (1009) working in Italian hospitals during the COVID-19 pandemic. https://www.selleckchem.com/products/3po.html The survey contained a 14-item resilience scale (RS14) and questionnaires to evaluate depression and anxiety symptoms. Non-HCP individuals (375) from the general population were used for comparison.
HCPs showed significantly lower resilience compared to the control group (= 0.001). No significant differences were observed after stratification for geographical area, work setting, role, or suspected/confirmed diagnosis of COVID-19. In a linear regression analysis, RS14 was inversely correlated with depression (R= 0.227, &lt; 0.001) and anxiety (R= 0.117, &lt; 0.001) and directly correlated with age (R= 0.012, &lt; 0.001) but not with body mass index (BMI, R= 0.002, = 0.213). In male HCPs, higher depression score (odds ratio (OR) 1.147, &lt; 0.001) or BMI (OR 1.136, = 0.011) significantly predicted having low resilience. In female HCPs, higher depression score (OR 1.111, &lt; 0.0001) and working in a COVID-19 free setting (OR 2.308, = 0.002) significantly predicted having low resilience. HCPs satisfied with personal protective equipment had higher levels of resilience (&lt; 0.010).
Our findings suggest that resilience was lower in Italian HCPs than in the general population after the first COVID-19 wave. Specific factors can be identified, and targeted interventions may have an important role to foster resilience of HCPs.
Our findings suggest that resilience was lower in Italian HCPs than in the general population after the first COVID-19 wave. Specific factors can be identified, and targeted interventions may have an important role to foster resilience of HCPs.